The paper was co-authored with Professor Kang G. Shin from the Electrical Engineering and Computer Science department at The University of Michigan and researchers from the Swiss Federal Institute of Technology Lausanne and University of Wisconsin-Madison.

Privacy policies are the primary channel companies use to inform users about their data collection and sharing practices. However, as most users know, these policies can be long and difficult to comprehend. Motivated by the security implications of these documents, the team developed an automated framework for analyzing privacy policies called Polisis. The tool enables scalable, dynamic, and multi-dimensional queries on natural language privacy policies.

Researchers built Polisis with 130,000 privacy policies and a hierarchy of neural-network classifiers that can account for both high-level and fine-detail features of privacy practices.

The framework was used to create two applications: a querying application that can assign privacy icons to a given policy, and PriBot, the first freeform question-answering chatbot for privacy policies. Their tests demonstrated that PriBot could produce a correct answer among its top three results for 82% of the test questions.